Question

我已经获得了以下格式的pandas数据框：

             datetime                               name  mtd  code
0 2017-09-07 00:00:08                        profile/log  GET  300
1 2017-09-07 00:00:17                        profile/log  PUT  300
3 2017-09-07 00:00:19                             unknown PUT  200
4 2017-09-07 00:00:21                   extras/dashboard  GET  300
5 2017-09-07 00:00:23                       extras/stats  GET  300
6 2017-09-07 00:00:26                 extras/dashboard    GET  300
7 2017-09-07 00:00:29         extras/authz-profile/check  GET  200
8 2017-09-07 00:00:34                              about  PUT  300
9 2017-09-07 00:00:36                         extras/fav  GET  304
2 2017-09-07 00:00:44                       extras/store  GET  200

我想做的是：

计算从2017-09-07 00:00:10到2017-09-07 00:00:40的每5秒间隔内每个名称 - mtd对的响应代码以3 开头的出现次数

理想的输出是：

     datetime_start     pair                      3??_count
2017-09-07 00:00:10     profile/log - GET         2
2017-09-07 00:00:15     -                         0
2017-09-07 00:00:20     extras/dashboard - GET    1
2017-09-07 00:00:20     extras/stats - GET        1
2017-09-07 00:00:25     extras/dashboard - GET    1
2017-09-07 00:00:30     about - PUT               1
2017-09-07 00:00:35     extras/fav - GET          1
2017-09-07 00:00:40     -                         0

我如何用 pandas 做到这一点？

我编写了一段代码来创建时间段，如desirable output表中所示，但不知道如何计算3？每个5秒钟的名称 - mtd对。我非常感谢任何帮助！

data['datetime_start'] = pd.date_range(start="2017-09-07 00:00:10", end="2017-09-07 00:00:40", freq="5S")

Answer 1

创建start_date列

df['start_date']= df[' datetime'].apply(lambda dt: datetime.datetime(dt.year, dt.month, dt.day, dt.hour,dt.minute ,5*(dt.second//5)))

然后你可以聚合

df.groupby(['start_date','name','mtd']).size()

Answer 2

这是解决这个问题的一种方法

创建一个组合了name-mtd的列，如下所示

df['pair'] = df['name']+' - '+df['mtd']

然后使用PeriodIndex指定将列数据时间分组的时间段，如下所示

res = df.groupby([pd.PeriodIndex(df.datetime.dt.round('5s'),freq='5S'),
                'pair'])['pair'].count()

输出

datetime             pair                            
2017-09-07 00:00:10  profile/log - GET                   1
2017-09-07 00:00:15  profile/log - PUT                   1
2017-09-07 00:00:20  extras/dashboard - GET              1
                     unknown - PUT                       1
2017-09-07 00:00:25  extras/dashboard - GET              1
                     extras/stats - GET                  1
2017-09-07 00:00:30  extras/authz-profile/check - GET    1
2017-09-07 00:00:35  about - PUT                         1
                     extras/fav - GET                    1
2017-09-07 00:00:45  extras/store - GET                  1
Name: pair, dtype: int64

Pandas，python：每5分钟计算唯一的名称 - 方法出现次数

2 个答案: